c0fc28e455
Per `feedback_isv_for_adaptive_bounds`, the hardcoded `warmup_gate = (fold_step_counter / WARMUP_STEPS_FALLBACK).min(1.0)` ramp violated the rule: adaptive bounds in ISV, never hardcoded constants. The variance-driven k_aux/k_q sigmoid steepness already provides warmup behavior intrinsically: - High variance (cold-start, EMAs still moving) → k → K_MIN → flat sigmoid → gate ≈ 0.5 regardless of input. That IS the warmup. - Low variance (settled) → k → K_BASE → sharp sigmoid → gates respond correctly to driver signals. Adding a separate hardcoded step-counter multiplier on top was double-counting + tuning-driven (the 1000-step threshold had no principled basis). Removed entirely. Removed (per `feedback_no_partial_refactor`, all atomically): - `WARMUP_STEPS_FALLBACK` constant in `sp14_isv_slots.rs` - `warmup_gate: f32` parameter in `alpha_grad_compute_kernel.cu` - `gate1 * gate2 * warmup_gate` → `gate1 * gate2` in kernel - `warmup_gate` arg from `launch_sp14_alpha_grad_compute` - `fold_step_counter: usize` field on the trainer struct - `fold_step_counter = 0` reset in `reset_for_fold` - `fold_step_counter` init in trainer constructor - `let warmup_gate: f32 = 1.0;` and `.arg(&warmup_gate)` from B.4 oracle tests (4 launches: 2 in alpha_grad_schmitt_hysteresis, 20 in alpha_grad_adaptive_beta loop) Build: clean, 18 warnings (pre-existing baseline). Tests: cargo test --no-run on sp14_oracle_tests succeeds. Net result: EGF gate's warmup behavior now lives entirely in the variance-driven k_aux/k_q sigmoid steepness controller (ISV slots 388/var_aux, 389/var_q). No hardcoded step counter. Honors `feedback_isv_for_adaptive_bounds` and `pearl_controller_anchors_isv_driven`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.